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\section{Conclusion}
\label{sec:conclusion}
Since the data set was large and processing the dataset took a long time, we broke down the processes into 3 different parts - 
loading the data, preprocessing and validation/regression. This avoided preprocessing the data again and again. Although it took a long time 
to obtain the final model, the RapidMiner operators like remove useless features reduced the processing. Also using a high ridge parameter of 
$(10^7)$ during development enabled us to filter the features based on their p-values. After many iterations of feature selections 
and we obtained a RMSE of $8.519$ with a ridge parameter of $160$ with a 10 fold cross validation. The final $17$ features that have been included in the model
have been explained to be important and almost the same as those picked by human knowledge.
 
